TY - GEN A1 - Sakizloglou, Lucas A1 - Khakharova, Taisiya A1 - Rühs, Florian A1 - Lambers, Leen T1 - On an exemplar supporting model-based quality assurance research for healthcare systems-of-systems T2 - SESoS '24: Proceedings of the 12th ACM/IEEE International Workshop on Software Engineering for Systems-of-Systems and Software Ecosystems N2 - As healthcare is being massively digitized, it increasingly relies on the harmonious interaction of autonomous and interconnected software systems. In this context, it becomes crucial to ensure that the emergent behavior that occurs when these systems interact remains beneficial to the overall quality goals. This task requires that the interacting systems are conceptualized and further studied as a System-of-Systems (SoS). We present the design and prototypical implementation of an exemplar for such a Healthcare SoS (HSoS) which consists of different types of basic healthcare systems. Its typical quality goals, e.g., safety and interoperability, and selected technical characteristics, e.g., cloud-nativity and support for integration with robotics, render the exemplar representative of modern healthcare solutions. The exemplar simulates patient journeys which rely on the interaction of the constituent systems and thereby may capture the emergent behavior of the HSoS. Our aim is that the exemplar supports future research on quality assurance for HSoSs using model-based techniques, which are suitable for developing both SoSs and safety-critical systems. For each quality goal, we outline relevant challenges as well as our research plans. KW - healthcare SoS KW - exemplar KW - model-based quality assurance Y1 - 2024 SN - 979-8-4007-0557-1 U6 - https://doi.org/10.1145/3643655.3643879 SP - 57 EP - 60 PB - Association for Computing Machinery CY - New York, NY, United States ER - TY - GEN A1 - Lambers, Leen A1 - Sakizloglou, Lucas A1 - Al-Wardi, Osama A1 - Khakharova, Taisiya ED - Harmer, Russ ED - Kosiol, Jens T1 - Taint analysis for graph APIs focusing on broken access control T2 - Graph Transformation, 17th International Conference, ICGT 2024, Held as Part of STAF 2024, Enschede, The Netherlands, July 10–11, 2024, Proceedings N2 - Graph APIs are capable of flexibly retrieving or manipulating graph-structured data over the web. This rather novel type of APIs presents new challenges when it comes to properly securing the APIs against the usual web application security risks, e.g., broken access control. A prominent security testing approach is taint analysis, which traces tainted, i.e., security-relevant, data from sources (where tainted data is inserted) to sinks (where the use of tainted data may lead to a security risk), over the information flow in an application. We present a first systematic approach to static and dynamic taint analysis for Graph APIs focusing on broken access control. The approach comprises the following. We taint nodes in the Graph API if they represent data requiring specific privileges in order to be retrieved or manipulated, and identify API calls which are related to sources and sinks. Then, we statically analyze whether tainted information flow between API source and sink calls occurs. To this end, we model the API calls using graph transformation rules. We subsequently use critical pair analysis to automatically analyze potential dependencies between rules representing source calls and rules representing sink calls. The static taint analysis (i) identifies flows that need to be further reviewed, since tainted nodes may be created by an API call and used or manipulated by another API call later without having the necessary privileges, and (ii) can be used to systematically design dynamic security tests for broken access control. The dynamic taint analysis checks if potential broken access control risks detected during the static taint analysis really occur. We apply the approach to a part of the GitHub GraphQL API. KW - Graph Transformation KW - Taint Analysis KW - Graph APIs KW - Security Testing Y1 - 2024 UR - https://link.springer.com/chapter/10.1007/978-3-031-64285-2_10 SN - 978-3-031-64284-5 SN - 978-3-031-64285-2 U6 - https://doi.org/10.1007/978-3-031-64285-2_10 IS - 14774 SP - 180 EP - 200 PB - Springer CY - Cham ER - TY - GEN A1 - Sakizloglou, Lucas A1 - Khakharova, Taisiya A1 - Lambers, Leen T1 - A graph-centric neuro-symbolic architecture applied to personalized sepsis treatments T2 - 2025 IEEE/ACM 1st International Workshop on Neuro-Symbolic Software Engineering (NSE) : proceedings N2 - Recent research on intelligent healthcare employs Deep Reinforcement Learning (DRL) to personalize treatments according to patients’ physiological characteristics and thus render treatments more effective. However, the majority of approaches rely on the relational data model, that struggles with the representation of the complex relationships within medical data. Moreover, the output of these approaches is typically a recommended action, e.g., a dosage; however, clinicians need the contextualization, i.e., the provision of supporting information, of such recommendations in order to decide whether to follow it.We present a neuro-symbolic architecture for personalized treatments based on a graph-centric foundation. The architecture is based on representing medical data as a knowledge graph and learning via graph neural networks; their combination enables the inherent capturing of relationships and their native integration into reasoning, which may thus render recommendations more effective. Moreover, the architecture employs formally specified graph queries over the knowledge graph to contextualize personalized treatments. We exemplify the architecture by an application to sepsis treatments and based on a widely-used medical dataset. KW - Intelligent healthcare KW - Graph neural networks KW - Graph queries KW - Knowledge graphs KW - Neuro-symbolic AI Y1 - 2025 UR - https://ieeexplore.ieee.org/document/11039286 SN - 979-8-3315-1461-7 SN - 979-8-3315-1460-0 U6 - https://doi.org/10.1109/NSE66660.2025.00009 SP - 11 EP - 16 PB - IEEE CY - Piscataway, NJ ER - TY - GEN A1 - Khakharova, Taisiya A1 - Salizloglou, Lucas A1 - Lambers, Leen T1 - Exploring a graph-based approach to offline reinforcement learning for sepsis treatment. T2 - arXiv N2 - Sepsis is a serious, life-threatening condition. When treating sepsis, it is challenging to determine the correct amount of intravenous fluids and vasopressors for a given patient. While automated reinforcement learning (RL)-based methods have been used to support these decisions with promising results, previous studies have relied on relational data. Given the complexity of modern healthcare data, representing data as a graph may provide a more natural and effective approach. This study models patient data from the well-known MIMIC-III dataset as a heterogeneous graph that evolves over time. Subsequently, we explore two Graph Neural Network architectures - GraphSAGE and GATv2 - for learning patient state representations, adopting the approach of decoupling representation learning from policy learning. The encoders are trained to produce latent state representations, jointly with decoders that predict the next patient state. These representations are then used for policy learning with the dBCQ algorithm. The results of our experimental evaluation confirm the potential of a graph-based approach, while highlighting the complexity of representation learning in this domain. KW - Machine learning KW - Computer science Y1 - 2025 U6 - https://doi.org/10.48550/arXiv.2509.03393 SP - 1 EP - 20 PB - arXiv CY - Ithaca, NY ER -